About the role
About Pipe Pipe builds embedded capital for small businesses everywhere: credit products delivered inside the software platforms SMBs already use to run their businesses (think vertical SaaS, payments platforms, and marketplaces). We're growing fast, with 250% ARR growth year over year, new partnerships with some of the world's biggest brands, and expansion into new global markets. How it works : merchants get an offer on the platform, understand it in seconds, accept it in four clicks, and pay automatically as they earn. Behind that simplicity sits real credit risk, partner SDKs and APIs, money movement infrastructure, and capital markets infrastructure. We're a remote-first team that ships fast, has high trust of our platform partners like Uber Eats, holds each other to a high bar, and leaves egos at the door.
The Role We're looking for a Head of Risk, Decision Science & Portfolio Strategy leader to take end-to-end ownership of Pipe's credit strategy and risk outcomes. This is a senior, hands-on leadership role. You won't just oversee strategy; you own the data chain; from raw, messy ingestion to final credit decisioning. You are the architect of our risk infrastructure, responsible for identifying data gaps, driving the
requirements to acquire net-new signals, and ensuring absolute data integrity. You create clarity and momentum in fast-moving environments by rolling up your sleeves to troubleshoot issues, refine models, and build the systems that connect technical outputs to real-world business results. The right person combines analytical rigor with pragmatism, urgency with judgment, and the grit to deliver meaningful results fast. You're comfortable rolling up your sleeves, working through imperfect data and systems, and driving accountability without burning out
the team. You create clarity and momentum in fast-moving environments. Success in the first three months will be measured by concrete process and policy improvements expected to drive portfolio performance, along with high trust earned with internal and external stakeholders. Over time, you'll help scale Pipe's risk infrastructure and modeling capabilities. But the immediate focus is clear: operate the credit strategy with discipline, speed, and ownership of outcomes. What you’ll do Model Deployment & Performance Own the performance and outcome of all deployed statistical and machine learning models, ensuring a seamless translation from data science output to effective underwriting decisions and measurable portfolio results. Establish rigorous monitoring and feedback loops between model outputs and real-world credit outcomes. Drive use of alternative data (transaction-level signals, partner platform data, behavioral indicators) to improve model accuracy and expand approval rates. Own model risk governance including model documentation, validation processes, and ongoing performance standards across all deployed models. Credit Strategy & Underwriting Direct the end-to-end credit strategy, translating risk models and data insights into a cohesive set of underwriting policies, approval strategies, and pricing frameworks to deliver target loss and growth performance. Own approval logic, pre-qualification strategies, and credit limit frameworks that balance growth targets, merchant experience, and credit loss thresholds. Set minimum acceptance criteria aligned with Pipe's risk appetite while optimizing approval rates, customer lifetime value, and capital facility performance. Design and implement pricing frameworks to ensure appropriate loss coverage and positive borrower selection. Portfolio Monitoring & Risk Management Own portfolio performance reporting and forward-looking risk adjustments across all credit products. Build early-warning systems and structured decision processes that enable fast, disciplined credit strategy adjustments. Ensure portfolio resilience across economic cycles through proactive monitoring and timely action. Data Integrity & Acquisition Strategy Act as the primary owner of our data quality roadmap, identifying discrepancies and driving technical solutions to resolve them. Proactively identify and acquire net-new data sources (internal or third-party) to solve for missing signals and improve model accuracy. Translate business
